Version 20.2 by annedevismes on 2021/06/08 10:57
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1 == [[image:ilastik_logo.PNG||style="float:right"]] ==
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3 == [[image:Pixel_classification workflow.png||style="float:left"]](% style="color:#c0392b" %)Analysis approach for series of rodent-brain section image(%%) ==
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5 //Ilastik //is a versatile image analysis tool specifically designed for the classification, segmentation, and analysis of biological images based on supervised machine-learning algorithms.
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7 **There are two main approaches for the analysis of rodent-brain section image~:**
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9 1. pixel classification only (with two or more classes) and
10 1. pixel classification with two classes (//immunoreactivity// and //background//), followed by Object classification with two classes (//objects of interest// and //artefact//).
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12 **Which approach is best for my dataset?**
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14 As a general rule, pixel classification is suitable for images in which there are clear differences in the colour, intensity, and / or texture of the feature of interest (labelling) versus the background and other structures.  If there is non-specific labelling in the image that is very similar in appearance to the labelling of interest, object classification may allow the non-specific labelling to be filtered out on the basis of the object-level features such as shape and size. The best approach is determined by trial and error.
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16 === (% style="color:#c0392b" %)Pixel classification workflow(%%) ===
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18 For a quick introduction, [[watch this video>>https://www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be]].
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20 **Basic steps**
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22 * Train the classifier with two classes (labelling and background).
23 * Apply the classifier to the rest of the images (batch processing).
24 * Export the probability maps in HDF5 format and simple_segmentation images in PNG format with the default settings.
25 * Review the results.
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27 === (% style="color:#c0392b" %)Object-classification workflow(%%) ===
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29 There are three options on the //ilastik //start-up page for running Object Classification. Choose the //Object Classification with Raw Data and Pixel Prediction Maps //as input.
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31 * Save the object-classification file in the same folder as the raw images for analysis. If the images are moved after the //ilastik// file is created, the link between the //ilastik //file and the images may be lost, resulting in a corrupted file.
32 * In the "Input Data" applet, upload the original images and their respective probability maps in HDF5 format (output from the Pixel Classification).
33 * Train the classifier with two classes (labelling and artefacts).
34 * In the "Object Information Export" applet, export “Object Predictions” in PNG format.  Do not change the default export location.
35 * Review the results.